<h2>DESCRIPTION</h2>

<em>v.outlier</em> removes outliers in a 3D point cloud. By default, the outlier
identification is done by a bicubic spline interpolation of the
observation with a high regularization parameter and a low resolution
in south-north and east-west directions. Those points that differ in
an absolute value more than the given threshold from a fixed value,
reckoned from its surroundings by the interpolation, are considered as
an outlier, and hence are removed.

<p>
The <em>filter</em> option specifies if all outliers will be removed
(default), or only positive or only negative outliers. Filtering out
only positive outliers can be useful to filter out vegetation returns
(e.g. from forest canopies) from LIDAR point clouds, in order to
extract Digital Terrain Models. Filtering out only negative outliers
can be useful to estimate vegetation height.

<p>
There is a flag to create a vector that can be visualizated by
qgis. That means that topology is build and the z coordinate is
considered as a category.

<h2>EXAMPLES</h2>

<h3>Basic outlier removal</h3>

<div class="code"><pre>
v.outlier input=vector_map output=vector_output outlier=vector_outlier thres_O=25
</pre></div>

In this case, a basic outlier removal is done with a threshold of 25 m.

<h3>Basic outlier removal</h3>

<div class="code"><pre>
v.outlier input=vector_map output=vector_output outlier=vector_outlier qgis=vector_qgis
</pre></div>

Now, the outlier removal uses the default threshold and there is also
an output vector available for visualizaton in QGIS
 (<a href="http://www.qgis.org">http://www.qgis.org</a>).

<h3>North carolina location example</h3>

<div class="code"><pre>
v.outlier input=elev_lid792_bepts output=elev_lid792_bepts_nooutliers \
  outlier=elev_lid792_bepts_outliers ew_step=5 ns_step=5 thres_o=0.1
</pre></div>


<h2>NOTES</h2>

This module is designed to work with LIDAR data, so not topology is
built but in the QGIS output.

<h2>SEE ALSO</h2>

<em><a href="v.surf.bspline.html">v.surf.bspline</a></em>

<h2>AUTHORS</h2>

Original version of the program in GRASS 5.4:
<br>
Maria Antonia Brovelli, Massimiliano Cannata, Ulisse Longoni and Mirko Reguzzoni
<br><br>
Updates for GRASS 6:
<br>
Roberto Antolin

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